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Decoding the Data Ecosystem

Decoding the Data Ecosystem

Written by: Allissa Dillman
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 ABOUT THE PODCAST Host Bio Allissa Dillman, PhD, Training and Engagement Director for the CFDE Training Center, is the founder and CEO of BioData Sage LLC, a company focused on providing a holistic approach to data science integration in the biomedical and biological science fields. She works with clients in industry, academia, government, and the nonprofit sector to create and support training programs on bioinformatics, cloud computing, and the tools and standards for reproducible data science practices for scientific and lay communities. She also creates community events, such as hackathons, where broad communities work towards solving real biomedical data challenges. Dr. Dillman is a member of the adjunct faculty at Montgomery College and has over 10 years of experience working for the National Institutes of Health (NIH). Her work focuses on lowering the barriers of entry for data science and cloud computing. She received her PhD in computational neuroscience as part of the graduate partnership program between NIH and the Karolinska Institute, Sweden. Why Listen? Decoding the Data Ecosystem: A CFDE Training Center Podcast is more than just a podcast; it's a community for anyone passionate about the potential of omics research to solve complex biological puzzles and address pressing health challenges. Whether you're a seasoned researcher, a student just starting out, or simply curious about the future of biology, this podcast offers valuable insights, inspiring stories, and practical advice to guide your journey through the world of omics research training and education. Availability Find Decoding the Data Ecosystem on your favorite podcast platform, including Spotify, Apple Podcasts, Google Podcasts, and more. Subscribe today to stay updated with the latest episodes and join the conversation shaping the future of omics research training and education. This podcast is hosted by Oak Ridge Associated Universities (ORAU). Learn more at orau.org.Copyright 2026 Decoding the Data Ecosystem Biological Sciences Political Science Politics & Government Science
Episodes
  • Episode 14: Investigating Acute to Chronic Pain Signatures (A2CPS) for Advancing Pain Prevention Strategies

    Episode 14: Investigating Acute to Chronic Pain Signatures (A2CPS) for Advancing Pain Prevention Strategies
    Feb 4 2026

    Episode 14: Investigating Acute to Chronic Pain Signatures (A2CPS) for Advancing Pain Prevention Strategies

    Description

    In this episode, Allissa Dillman chats with Margaret Taub, a Senior Scientist with the Johns Hopkins Bloomberg School of Public Health on her journey from mathematics to statistical genetics, highlighting her current role with the Acute to Chronic Pain Signatures (A2CPS) program. The A2CPS study aims to develop predictive biomarkers for chronic pain by collecting comprehensive genomic, psychosocial, and functional data from patients. To coordinate diverse data sources, Margaret emphasized the importance of collaboration and interdisciplinary teamwork, drawing parallels between her experiences in teaching, music, and Ultimate Frisbee to her approach in science. Margaret and Allissa also discuss the study's progress and future plans.

    Guest Bio

    Margaret Taub, PhD, is a Senior Scientist at Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health. She works collaboratively with clinicians and epidemiologists to use genetic and genomic data to understand the causes of complex diseases. She is also an Investigator with the Data Integration and Resource Center for the Acute to Chronic Pain Signatures (A2CPS) program.

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    48 mins
  • Episode 13: Harnessing Artificial Intelligence and Machine Learning for Statistical Analysis in Genomic Data

    Episode 13: Harnessing Artificial Intelligence and Machine Learning for Statistical Analysis in Genomic Data
    Jan 9 2026

    Description

    In this episode, Allissa Dillman talks with John Kwagyan about the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in genomics and personalized medicine as well as the promise of these technologies in analyzing complex biological data to advance disease prediction, prevention, and personalized treatments. They also discuss machine learning models, the differences between machine learning and statistical learning, explainable AI, and ethical considerations, as well as the skills future researchers will need to thrive in the AI-genomics landscape.

    Guest Bio

    John Kwagyan, PhD, is a Statistician and Graduate Associate Professor of Public Health at Howard University College of Medicine, and serves as co-Director of Biostatistics, Epidemiology and Research Design (BERD) at the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS). He is co-PI of the Public Health Informatics and Technology program for District of Columbia (PHIT4DC), and PI (Data Science Core) of the recently funded Howard-Hopkins Comprehensive Alliance in Cancer Research and Education (H2CARE). His research interests include statistical genetics and predictive modelling of clustered data with applications to clinical and public health outcomes.
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    44 mins
  • Episode 12: Exploring the Human Reference Atlas (HRA) Organ Gallery in Immersive 3D

    Episode 12: Exploring the Human Reference Atlas (HRA) Organ Gallery in Immersive 3D
    Dec 15 2025
    Description In this video podcast, Allissa Dillman and Andreas Bueckle discuss and demonstrate the Human Reference Atlas (HRA) Organ Gallery, a virtual reality (VR) application that lets users explore the HRA in immersive 3D. Andreas also presents the “HRA Powers of Ten,” a data integration module in development that uses the HRA Common Coordinate Framework (CCF) to harmonize, visualize, and explore data from the small/large intestine, lymph node, skin, and liver. The HRA Organ Gallery is available, for free, to anyone at https://www.meta.com/experiences/5696814507101529. A paper describing the concept of the HRA Organ Gallery is available at https://doi.org/10.3389/fbinf.2023.1162723. Single-cell atlassing portals such as the Human BioMolecular Atlas Project (HuBMAP), the Cellular Senescence Network (SenNet), and the Human Tumor Atlas Network (HTAN) publish datasets across scales for multiple organs of the adult human body in health, aging, and disease. This data can be interrogated semantically and spatially, usually on 2D screens with limited 3D affordances. Imagine, for example, examining the 3D location of a cell in the context of a tissue or zooming across multiple orders of magnitude. To be tackled effectively, 3D problems require 3D platforms. Guest Bio Andreas Bueckle, Ph.D. (https://andreas-bueckle.com), is the Research Lead in the Cyberinfrastructure for Network Science Center at the Luddy School of Informatics, Computing, and Engineering (SICE) at Indiana University in Bloomington, IN. His research interest is interactive information visualization in XR. Andreas has a TEDx talk titled “Living and Learning in the Metaverse” (available on YouTube and on the TED website). From 2023-2025, he was awarded two JumpStart Fellowships by the National Institutes of Health (NIH) to advance multiscale exploration of the human body in VR with the Human Reference Atlas Organ Gallery. In 2025, he received an R03 award, also by the NIH, to advance the integration of 3D reference organs with data visualizations of cell type populations for Common Fund datasets in 3D and VR.
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    47 mins
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